So sánh phương pháp

	Hồi quy Logistic bán giám sát	Lan truyền nhãn
Lĩnh vực	Học máy	Học máy
Họ	Machine learning	Machine learning
Năm ra đời	1995–2000	2002
Người khởi xướng	Nigam, K.; McCallum, A. et al. (EM variant); Yarowsky, D. (self-training)	Zhu, X. & Ghahramani, Z.
Loại	Semi-supervised classifier	Graph-based semi-supervised classification
Công trình gốc	Nigam, K., McCallum, A., Thrun, S., & Mitchell, T. (2000). Text classification from labeled and unlabeled documents using EM. Machine Learning, 39, 103–134.	Zhu, X., & Ghahramani, Z. (2002). Learning from labeled and unlabeled data with label propagation. Technical Report CMU-CALD-02-107, Carnegie Mellon University.
Tên gọi khác	SSL logistic regression, semi-supervised LR, EM logistic regression, self-training logistic classifier	LP, label spreading, graph-based semi-supervised learning, harmonic label propagation
Liên quan	5	3
Tóm tắt	Semi-supervised logistic regression extends the standard logistic classifier by incorporating unlabeled data during training. Using self-training, expectation-maximization, or label-propagation wrappers, it iteratively assigns soft labels to unlabeled examples and refines model parameters, improving generalization when labeled data are scarce relative to the full dataset.	Label Propagation is a graph-based semi-supervised learning algorithm introduced by Zhu and Ghahramani in 2002 that spreads class labels from a small set of labeled nodes to a large set of unlabeled nodes by iteratively diffusing label information along the edges of a similarity graph, exploiting the manifold structure of the data.